ORIGINAL RESEARCH
published: 22 November 2016
doi: 10.3389/fpls.2016.01741
Vegetative and Adaptive Traits
Predict Different Outcomes for
Restoration Using Hybrids
Philip A. Crystal 1,2 , Nathanael I. Lichti 1 , Keith E. Woeste 1,3 and Douglass F. Jacobs 1*
1
Hardwood Tree Improvement and Regeneration Center, Department of Forestry and Natural Resources, Purdue University,
West Lafayette, IN, USA, 2 Department of Biology, Colby College, Waterville, ME, USA, 3 Hardwood Tree Improvement and
Regeneration Center, Northern Research Station, USDA Forest Service, West Lafayette, IN, USA
Edited by:
Badri Padhukasahasram,
Illumina, USA
Reviewed by:
Jiasheng Wu,
Zhejiang A & F University, China
Jinfeng Chen,
University of California, Riverside, USA
*Correspondence:
Douglass F. Jacobs
[email protected]
Specialty section:
This article was submitted to
Evolutionary and Population Genetics,
a section of the journal
Frontiers in Plant Science
Received: 26 August 2016
Accepted: 04 November 2016
Published: 22 November 2016
Citation:
Crystal PA, Lichti NI, Woeste KE and
Jacobs DF (2016) Vegetative
and Adaptive Traits Predict Different
Outcomes for Restoration Using
Hybrids. Front. Plant Sci. 7:1741.
doi: 10.3389/fpls.2016.01741
Hybridization has been implicated as a driver of speciation, extinction, and invasiveness,
but can also provide resistant breeding stock following epidemics. However, evaluating
the appropriateness of hybrids for use in restoration programs is difficult. Past the
F1 generation, the proportion of a progenitor’s genome can vary widely, as can the
combinations of parental genomes. Detailed genetic analysis can reveal this information,
but cannot expose phenotypic alterations due to heterosis, transgressive traits, or
changes in metabolism or development. In addition, because evolution is often driven
by extreme individuals, decisions based on phenotypic averages of hybrid classes may
have unintended results. We demonstrate a strategy to evaluate hybrids for use in
restoration by visualizing hybrid phenotypes across selected groups of traits relative
to both progenitor species. Specifically, we used discriminant analysis to differentiate
among butternut (Juglans cinerea L.), black walnut (J. nigra L.), and Japanese walnut
(J. ailantifolia Carr. var. cordiformis) using vegetative characters and then with functional
adaptive traits associated with seedling performance. When projected onto the
progenitor trait space, naturally occurring hybrids (J. × bixbyi Rehd.) between butternut
and Japanese walnut showed introgression toward Japanese walnut at vegetative
characters but exhibited a hybrid swarm at functional traits. Both results indicate that
hybrids have morphological and ecological phenotypes that distinguish them from
butternut, demonstrating a lack of ecological equivalency that should not be carried into
restoration breeding efforts. Despite these discrepancies, some hybrids were projected
into the space occupied by butternut seedlings’ 95% confidence ellipse, signifying that
some hybrids were similar at the measured traits. Determining how to consistently
identify these individuals is imperative for future breeding and species restoration efforts
involving hybrids. Discriminant analysis provides a useful technique to visualize past
selection mechanisms and current variation in hybrid populations, especially when key
ecological traits that distinguish progenitors are unknown. Furthermore, discriminant
analysis affords a tool to assess ecological equivalency of hybrid populations and
breeding program efforts to select for certain traits and monitor the amount of variability
of those traits, relative to progenitors.
Keywords: discriminant analysis, ecological equivalency,
differentiation, hybridization, introgression, Juglandaceae
Frontiers in Plant Science | www.frontiersin.org
1
ecophysiology,
forest
restoration,
habitat
November 2016 | Volume 7 | Article 1741
Crystal et al.
Discriminant Analyses for Restoration
hybrid phenotypes can exceed, be intermediate to, equivalent
to, or below one or both progenitors, and that the proportion
of hybrid individuals expressing these trait levels varies among
hybrid generations: F1, F2, BC1F1. As such, grouping hybrids by
families (which may contain various hybrid classes depending
on paternity, recombination, and independent assortment)
or by generation for statistical analysis based on class-level
trait averages is not particularly meaningful. Furthermore, the
average performance of an entire class is relatively unimportant
as natural selection acts at the individual level. Even in a class
with poor overall fitness, natural selection may ensure that a few
exceptional individuals proliferate, as in the classical examples
of hybrid speciation in Helianthus and Iris (Arnold and Martin,
2010).
We present an application of multivariate discriminant
analysis (Legendre and Legendre, 2003) to identify the current
trajectory of a pool of hybrids through visualization of
individual hybrids in the context of the progenitors’ traitspace. Discriminant analysis is particularly useful for assessing
hybrid phenotypic selection mechanisms because it determines
which variables from a candidate set most effectively describe
differences among groups (i.e., progenitor species). Assuming the
variables selected are those that selection is operating on, the
ecological implications of those differences can be interpreted
based on the environment that hybrids might be deployed
into. While discriminant analysis has been previously used for
morphometric analysis of hybrid taxonomy (Mayer and Mesler,
1993; Burgess et al., 2005, Barbour et al., 2007), it has not been
used to investigate variation in functional traits that provide
little a priori information on taxonomic status, but do pertain
more directly to individual fitness (e.g., biomass or leaf area in
plants; innate behaviors, pheromone production, or body mass
in animals). The ability to simultaneously evaluate hybrids at
multiple, often correlated traits is especially important, as fitness
is not defined by a single character or by appearance per se,
but rather by the interplay among traits that allow continued
growth, survival, and most importantly, contributions to future
generations.
In the context of hybridization between progenitors with
distinct phenotypes, and assuming that genetic contributions are
equal and that the environment is constant, the overall hybrid
population would be expected to express some derivation of four
basic trajectories (Figure 1). Directional selection toward one
progenitor would result in distinct clustering of hybrids from an
intermediate point between progenitors to the progenitor with
greater fitness (Figure 1A). If both progenitors contribute equally
to fitness (or if hybrids have greater fitness in an intermediate
environment) the hybrid cluster will be maintained intermediate
to progenitor clusters (Figure 1B). If hybrid speciation has
occurred, the cluster will also be distinct, but may occupy a
separate area of the parameter space (Figure 1C). Finally, if
limited or no selection has occurred, or if F1 hybrid fitness
facilitates the establishment of future hybrid generations (Drake,
2006), hybrids will not cluster out at all and will occupy up to
the full parameter space between progenitor clusters (Figure 1D).
They may also occupy novel trait spaces. In each instance,
the size and shape of the hybrid cluster will depend upon the
INTRODUCTION
Naturally occurring hybridization between native and exotic
species can dilute native gene pools and result in extinction,
especially of rare species (Levin et al., 1996; Rhymer and
Simberloff, 1996). Hybridization has also been implicated in
development of invasiveness, as increased genetic diversity is
correlated with adaptation to environmental variation (Ellstrand
and Schierenbeck, 2006). Conversely, hybridization has been
used to introduce anthropogenically desired traits for thousands
of years (Rieseberg and Carney, 1998), and naturally occurring
hybrids between susceptible, native taxa and resistant, exotic taxa
may present a means to introduce resistance to exotic pests and
pathogens in vulnerable species of concern (Michler et al., 2006).
Given that hybrids exist, their functionality influences the
degree to which they threaten native populations and is the
major factor in determining their effects on ecosystem integrity.
Depending on the interplay between the fitness contributions of
genetic material from both progenitors relative to the selection
environment, hybrids may (1) introgress toward one of the
progenitor species or (2) persist as an intermediate group,
as is the case with most natural hybrid zones (Rhymer and
Simberloff, 1996; Rieseberg and Carney, 1998, Campbell and
Waser, 2001; Kimball et al., 2008). Alternatively, novel gene
combinations or interactions among genetic contributions with
selection (or polyploidy) may allow hybrids to (3) undergo
speciation. Unless selection reduces genetic variability, hybrids
may also (4) proliferate into a hybrid swarm characterized by high
phenotypic variability among individuals with extremely diverse
genotypes (Rhymer and Simberloff, 1996; Rieseberg and Carney,
1998, Soltis and Soltis, 2009). The actual trajectory taken by a
particular hybrid population also depends on the demography
and mating system of its progenitors. Under some circumstances
stochastic breeding has the potential to swamp out fitness effects
(Rhymer and Simberloff, 1996). In such cases, hybrid populations
can present as trajectories 1, 2, or 4.
Identifying which trajectory a hybrid population is currently
on would provide important information to evaluate how serious
a threat hybridization poses to rare species (Levin et al., 1996;
Rhymer and Simberloff, 1996) and overall ecosystem health.
Assessing the hybrid trajectory is therefore critical to the study
and management of hybridizing populations, yet is difficult to
accomplish using genetic analysis alone. The percent admixture
(Boecklen and Howard, 1997), class (Anderson and Thompson,
2002), or entire sequence of a hybrid individual does little to
reveal heterosis (hybrid vigor) and other non-additive genetic
effects (Chen, 2010). Genetic data also reveal little regarding how
differences in physiology and metabolism would affect secondary
metabolite production (Cheng et al., 2011) or transgressive
phenotypes (Rieseberg and Carney, 1998).
Physical studies can reveal non-additive phenotypic
effects, but are complicated by the variability associated
with hybridization. Typically, such studies group hybrids by class
and then regress the performance of different classes against
their mean scores for one or several variables (Diskin et al.,
2006; Crystal and Jacobs, 2014). Cheng et al. (2011) demonstrate
in the context of herbivore resistance, that for any given trait,
Frontiers in Plant Science | www.frontiersin.org
2
November 2016 | Volume 7 | Article 1741
Crystal et al.
Discriminant Analyses for Restoration
FIGURE 2 | Demography effects on hybrid phenotypes. Independent of
selection, large contributions of species A relative to species B to future
generations would skew the hybrids toward species A (A). Equal contributions
between species A and B to future generations would pull the hybrid pool
equally in both directions (B). Breeding among hybrid generations and
parental species would increase variability with some directionality toward
progenitors (C). Breeding solely among hybrids would increase variability
independent of progenitor phenotypes (D). Arrow size indicates size of
genetic contribution.
FIGURE 1 | Diagrammatic examples of four hybrid trajectories on an
undefined parameter space representing combinations of functional
adaptive traits: directional introgression (A), maintenance as an
intermediate group (B), speciation (C), and the hybrid swarm effect (D), which
operates under limited or no selection. Circle size indicates overall variability
and labels denote defined species. Gray arrows indicate selection, with arrow
size representing effect size.
strength of the fitness component and the strength and direction
of selection. The demography of progenitor populations, the
number of hybrid generations in coexistence and their fecundity
can mask selection effects (Figure 2).
To illustrate this approach to hybrid assessment, the trajectory
of naturally occurring hybrids (Juglans × bixbyi Rehd.), between
butternut (J. cinerea L.) and heartnut (J. ailantifolia Carr. var.
cordiformis) is examined using suites of qualitative vegetative
descriptors and quantitative functional adaptive traits drawn
from the literature. Black walnut (J. nigra) was included as an
outgroup to expand the trait combination space and ensure a
two-dimensional ordination. Results from both trait suites are
discussed in the context of the potential ecological equivalence
of J. × bixbyi to butternut for conservation.
based upon the maturation age of butternut (20 years) and
Japanese walnut (10 years), it is likely that several generations
of hybridization have taken place over the last century (Bonner,
2008). Hoban et al. (2009) found evidence for the existence
of F1, F2, and backcross hybrids from range-wide analysis.
Hybrids might be used as a source of genes for resistance
to butternut canker disease caused by the suspected exotic
pathogen (Furnier et al., 1999), Ophiognomonia clavigignentijuglandacearum (Michler et al., 2006), which has decimated
butternut populations (Ostry et al., 1994). However, Crystal and
Jacobs (2014) found discrepancies between hybrids and both
progenitors in terms of tolerance to flooding and drought. The
extent to which the deployment of hybrids would be a practical or
ecologically sensible method to address the demographic collapse
of butternut is therefore unclear.
MATERIALS AND METHODS
Vegetative Characters and Functional
Adaptive Traits
Study Taxa
All four taxa were characterized at 33 vegetative morphological
characters and functional adaptive traits (Table 1). The majority
of the vegetative characters were taken from the Descriptors for
Walnut (Juglans spp.) (IJD: IPGRI, 1994): leaf color, margin,
and shape; rachis color and pubescence; average number of
leaflets per leaf; and stem pubescence. To ensure distinction
between J. cinerea and J. ailantifolia, three traits were chosen from
Farlee et al. (2010): lenticel shape, lenticel density, and leaf scar
notching. Lenticel density was determined quantitatively in a 1cm2 area, between the fourth and fifth node from the shoot apex.
Naturally occurring hybrids between butternut and heartnut
present an ideal system to explore the use of discriminant
analysis on adaptive trait variation. Heartnuts are a horticultural
variety of Japanese walnut. Butternut and Japanese walnut are
heterodichogamous members of the Juglandaceae, subsection
Cardiocaryon (Stout, 1928; Fjellstrom and Parfitt, 1994, Kimura
et al., 2003). Japanese walnut was introduced to North America
in the late 1800s (Woeste and Pijut, 2009) and evidence of
hybridization was first reported in 1916 (Bixby, 1919). Thus,
Frontiers in Plant Science | www.frontiersin.org
3
November 2016 | Volume 7 | Article 1741
Crystal et al.
Discriminant Analyses for Restoration
Several other characteristics: initial shoot color and pubescence,
leaf texture (glabrous, rough, or scabrous), and rachis texture
(viscid or not viscid) were also assessed based on their potential
to serve as vegetative descriptors or because they appeared to vary
among seedlings of the taxa grown in the greenhouse.
The majority of functional adaptive traits were associated
with seedling survival or quality assessment (Wilson and Jacobs,
2006; Grossnickle, 2012): height, root collar diameter, root
volume, SPAD chlorophyll content, and dry biomass ratios
of root to shoot. Height was assessed from soil medium
to shoot apex. Root collar diameter was averaged from two
perpendicular measurements at soil medium. Root volume was
taken according to the volumetric method (Burdett, 1979) and
plant material for dry biomass ratios was dried to a constant
mass at 60◦ C. Other measures more associated with habitat
type were also assessed (Table 1). Photosynthetic rate and
transpiration were obtained on 23 and 24 August 2012 using
a LI-6400 portable photosynthesis system fitted with a 640002B LED light source. The LI-6400 was operated under default
settings with CO2 , PAR, and temperature set to 400 ppm CO2 ,
1300 µmol m−2 s−1 PAR, and 25.0◦ C. CO2 and PAR were
set high to exceed differential capabilities by species. Relative
humidity in the reference chamber was maintained at 44.4–
55.5%. Measurements were taken from 1100 to 1700 h as previous
measurements on a subset of seedlings found no variation
in photosynthetic rate from sun-up to sun-down (data not
shown). Measures were logged after A had plateaued for at
least 30 s.
TABLE 1 | Vegetative characters and functional adaptive traits with
transformations used in analysis.
Adaptive traits∗
Initial shoot color
3,4 Final
height
Initial shoot pubescence
3,4 Final
root collar diameter
1 Leaf
shape
Average leaf length
1 Leaf
margin
5 Average
leaf area
1 Leaf
color
6 Average
crown area
Leaf texture
Total leaf number
1 Average
number of
leaflets leaf−1∗
5 Estimated
1 Rachis
color
Estimated total leaf area stem−1
1 Rachis
pubescence∗
SPAD chlorophyll content
total leaf area
Rachis texture
5 Specific
1 Stem
Photosynthesis (µmol CO2 g−1 s−1 )
pubescence
2 Lenticel
shape
2 Lenticel
density∗
2 Leaf
scar notched
Number of nodes∗
leaf area (SLA, m2 g−1 )
SLA correlated photosynthesis (µmol CO2 m−2 s−1 )
Transpiration (mmol H2 O m−2 s−1 )
Water use efficiency (umol CO2 mmol−1 H2 O)
3,4 Root
volume
Foliar:rachis dry mass
3,4 Total
dry mass
3,4 shoot:root
dry mass
Asterisks (∗ ) denote quantitative variables. 1 IPGRI (1994), 2 Farlee et al. (2010),
3 Wilson and Jacobs (2006), 4 Grossnickle (2012), 5 Cornelissen et al. (2003),
6 Gauthier et al. (2013)
a single common scale. Individuals with missing trait values
(J. nigra = 2, J. × bixbyi = 4) were excluded from analysis.
Five J. × bixbyi individuals included in the adaptive trait
projection were excluded from the vegetative projection. Due
to transgressive rachis pubescence in hybrids, this trait was
encoded as a quantitative variable in the vegetative character
analysis. As traits in both analyses were carefully chosen a priori,
variables were not assessed for differences among pure species
using separate analysis of variance tests prior to running the
discriminant function (Legendre and Legendre, 2003).
Discriminant Analysis
In discriminant analysis, each sample (individual) is assigned
to a predefined group, in this case a progenitor species
or an outgroup. Variables are then analyzed for differences
among pre-defined groups and eigenvectors (eigenvector if
n = 2) that maximize the among group variance are generated,
with the constraints that each eigenvector must be a linear
combination of the sample traits, and that all eigenvectors must
be orthogonal to each other. The first step functions similarly
to a multivariate analysis of variance and the second is similar
to principle correspondence analysis (Legendre and Legendre,
2003). Discriminant analyses were performed using the lda
function in the MASS package in R (Venables and Ripley, 2002;
R Core Team, 2012).
To determine the hybrid trajectory, a discriminant analysis
was first performed on open-pollinated individuals of the
three walnut species (J. cinerea, J. ailantifolia, and J. nigra)
using the vegetative or adaptive trait suite. Data from these
individuals were then plotted on the discriminant axes and each
species cluster was enclosed within a 95% confidence ellipse
(confidenceEllipse function in the car package: Fox and Weisberg,
2011). Data from sampled hybrids were then projected onto the
species biplot to visualize their location on the discriminant axes
(Barbour et al., 2007).
Prior to discriminant analysis, quantitative variables were
visually inspected for normality using histograms. Only
√
shoot:root dry mass required transformation ( [shoot:root+1]).
Variables were then standardized to z-scores to place traits on
Frontiers in Plant Science | www.frontiersin.org
Vegetative characters
Plant Material and Planting
Seeds from J. cinerea, J. nigra, and J. × bixbyi were collected
in fall, 2011 from provenances archived at research plantations
and from naturally occurring trees throughout Indiana, USA
(Table 2). J. ailantifolia var. cordiformis seed from six of the
most common cultivars (Table 2) was obtained from Grimo
Nut Nursery (Ontario, Canada, 43◦ 150 N, 79◦ 090 W). After
collection, seeds were hulled, washed, and allowed to air dry.
On December 19, 2012 (J. cinerea, J. nigra, and J. × bixbyi)
and December 20, 2012 (J. ailantifolia) seeds were rinsed in
cold water, packed into bags filled with moistened, sifted peat
moss by family, and cold stored at 5◦ C at the Purdue University
Wright Forestry Center (West Lafayette, IN, USA, (40◦ 250
N, 87◦ 020 W). For measurement of seed characteristics (data
not shown), on April 18, 2012 all seeds were unpacked, rinsed
in cold water to remove excess peat, and returned to cold
storage.
To account for both interfamilial and species variation in
seedling emergence time and the subsequent effects on growth
4
November 2016 | Volume 7 | Article 1741
Crystal et al.
Discriminant Analyses for Restoration
media and placed in the greenhouse set to maintain 26.7/18.3◦ C
(day/night) on April 23, 2012. Seeds were removed and re-sown,
every 3 days to visualize emergence. Seeds with radicle emergence
less than 5 mm were wrapped in wet burlap to prevent desiccation
and returned to the cooler at 5◦ C.
On May 22, 2012, seeds were planted as a completely
randomized design into the top 7 cm of 9.63-L pots
(TP818, Stuewe and Sons, Tangent, OR, USA), pre-filled
with 3783.9 ± 184.1 g of MM 560 mixed with 48.2 g of Osmocote
Plus 15-9-12, 8–9 month controlled-release fertilizer. Pots were
immediately watered twice to allow for soil settling and prevent
seed desiccation. Emergence occurred from May 29 to June 2,
2012, after which, non-germinating seedlings were discarded.
Until emergence, pots were lightly watered each day to ensure
the top of the pot (and the seed) remained moist; watering then
occurred every 2–3 days to maintain field capacity. On July 6,
2012, chlorophyll mutants were also discarded. Seedlings were
moved from benches to a cinderblock grid on the floor from July
28 to 29, 2012. Defoliation occurred from 15 to 21 September
2012, after which watering ceased. Plants were destructively
harvested for biomass measurements and root volume on
November 2, 2012. From May 22 to September 21, 2012,
average temperature and relative humidity were (mean ± SD):
25.7 ± 6.1◦ C and 30.1 ± 7.1%.
TABLE 2 | Family, provenance, and number of sample replicates for the
four taxa included in the experiment: Juglans ailantifolia Carr. var.
cordiformis (family = 6: total n = 21), J. cinerea L. (8: 37), J. nigra L. (11:
39), and J. × bixbyi Rehd. (16: 79).
Taxon
Juglans ailantifolia Carr.
var. cordiformis
Family
Provenance
n1
Bates
–
4
Simcoe 8-2
–
6
Fodermaier
–
5
Campbell CW1
–
3
1
R
Juglans cinerea L.
Juglans nigra L.
Juglans × bixbyi Rehd.
1 Half-sib
Imshu
–
Locket
–
2
712
Whitewater, WI, USA
3
717
Whitewater, WI, USA
2
719
Whitewater, WI, USA
10
724
Whitewater, WI, USA
5
741
Whitewater, WI, USA
5
766
Whitewater, WI, USA
2
784
Plymouth, IN, USA
7
927
Laona, WI, USA
1
1622
Pembine, WI, USA
2
502
KS
4
504
IA
6
513
IA
8
514
KS
1
516
KY
5
517
IA
6
520
IA
2
525
IA
2
527
IL
1
528
IL
3
529
Unknown
1
701
Rochester, IN, USA
2
735
Sanford, ME, USA
4
745
Scotland, ON, Canada
4
780
Plymouth, IN, USA
4
781
Plymouth, IN, USA
7
782
Plymouth, IN, USA
3
803
Angola, IN, USA
5
890
Twinsburg, OH, USA
8
1001
Clermont, KY, USA
2
1061
West Lafayette, IN, USA
9
1062
Culver, IN, USA
6
1064
West Lafayette, IN, USA
9
1065
West Lafayette, IN, USA
7
1066
West Lafayette, IN, USA
6
1093
Augusta, MI, USA
3
RESULTS
Seedlings of J. ailantifolia, J. cinerea, and J. nigra were effectively
differentiated by the eigenvectors generated from both the
vegetative and adaptive discriminant analyses (Figures 3 and 4).
The two eigenvectors for each analysis described essentially
all of the variation in their respective datasets (proportion of
trace: Table 3). Trait loadings were relatively high for two
vegetative traits: initial shoot color and leaf texture (Table 3). No
adaptive traits were exceptional at discriminating species, with
total biomass describing the most variation across both linear
discriminant analyses (LDAs, Table 3).
Seedlings of J. ailantifolia, J. cinerea, and J. nigra clustered
tightly in the vegetative trait LDA (Figure 3A). There was no
overlap among 95% confidence ellipses, and J. ailantifolia–J.
cinerea were separated the least by the canonical discriminant
functions, perhaps reflecting taxonomic proximity. The J. cinerea
ellipse (Figure 3A) had the greatest dispersion overall, potentially
reflecting larger genetic variation among sampled individuals
relative to the other species. When hybrids were applied onto
the parameter space, most individuals fell on a gradient between
J. ailantifolia and J. cinerea ellipses (Figure 3B). Roughly 50% of
the hybrids fell within the J. ailantifolia ellipse, with 21% in the
J. cinerea ellipse, and 23.5% intermediate in a space not occupied
by either progenitor species’ ellipse (Table 4).
Clusters for J. ailantifolia, J. cinerea, and J. nigra based on
functional adaptive traits were fairly distinct and reasonably well
separated (Figure 4A). There was some overlap between J. cinerea
and J. nigra ellipses, with both equivalent distances from the
J. ailantifolia ellipse. Hybrids projected onto the parameter space
were widely and relatively evenly dispersed (Figure 4B). The
individuals represented in each family.
(Seiwa, 2000), stratification was extended past the recommended
90–120 days for J. cinerea and J. nigra (Bonner, 2008) to 126 (J.
cinerea, J. nigra, J. × bixbyi) and 125 (J. ailantifolia) days. To
further minimize emergence time variation and avoid potting
unviable seed, all material was pre-germinated following the
methods of Woeste et al. (2011). Seeds were sown in flats
containing Scotts Metro-Mix 560 with Coir (MM 560) growing
R
Frontiers in Plant Science | www.frontiersin.org
5
November 2016 | Volume 7 | Article 1741
Crystal et al.
Discriminant Analyses for Restoration
FIGURE 3 | Canonical discriminant analysis separating Juglans ailantifolia Carr. var. cordiformis (JA), J. cinerea L. (JC), and J. nigra L. (JN) based
upon vegetative characteristics (A). J. × bixbyi Rehd. (JX) individuals (open circles) were applied to the parameter space using the discriminant function
determined from (A). (B) Pure species are enclosed by 95% confidence interval ellipses. LD1 and LD2 refer to the first and second discriminant axes, respectively.
FIGURE 4 | Canonical discriminant analysis separating J. ailantifolia Carr. var. cordiformis, J. cinerea L., and J. nigra L. based upon functional
adaptive traits (A). J. × bixbyi Rehd. individuals were applied to the parameter space as Figure 3 (B).
largest proportion (36.6%) of hybrids fell within the J. nigra
ellipse including the overlap with the J. cinerea ellipse. If
the combined proportion of individuals occupying the overlap
between the J. cinerea and J. nigra ellipses were excluded, most
23.3% of the total hybrids occupied the J. ailantifolia parameter
space (Table 4). Only 16.4% (28.7%, including J. cinerea/J. nigra
overlap space) of hybrids fell within the J. cinerea ellipse.
Furthermore, 17.8% of hybrids represented novel combinations
Frontiers in Plant Science | www.frontiersin.org
of progenitor traits and fell outside the parameter space occupied
by J. ailantifolia, J. cinerea, and J. nigra ellipses.
Hybrid projections in the vegetative and adaptive trait LDAs
were not synonymous. Of the 73 hybrids remaining after
exclusion of individuals with missing data points in the adaptive
analysis, only 11 (15.1%) fell within J. ailantifolia space and
4 (5.5%) fell within J. cinerea space in both analyses. Halfsib progenies were fairly consistent in agreement across the
6
November 2016 | Volume 7 | Article 1741
Crystal et al.
Discriminant Analyses for Restoration
TABLE 3 | Loading values for vegetative and adaptive traits as assigned by canonical discriminant analysis.
Vegetative trait loadings1
LD1
LD2
Average leaflet number
0.056
−0.231
0.857
−1.692
Number of nodes
0.090
0.335
Diameter
−0.994
−1.380
−0.099
0.014
Average leaf length
−0.257
0.799
0.006
0.020
Average crown area
0.455
−0.103
Lenticel density
Rachis pubescence
Initial shoot (IS) color intermediate
Adaptive trait loadings2
Height
LD1
LD2
0.097
−1.037
Leaf number
1.073
−0.276
IS color red
−2.309
−4.228
Est. total leaf area
−0.764
−0.309
IS pubescence no
−0.651
−1.372
Leaf area stem−1
0.481
−0.501
IS pubescence intermediate
0.009
−1.139
Average leaf area
0.231
−0.622
IS yes
1.702
−1.783
1 SPAD
0.130
−0.379
Leaf shape broad elliptic
0.846
−0.717
Specific Leaf Area (SLA)
1.026
−1.288
Leaf margin dentate
0.352
−0.013
SLA correlated A
Leaf color green
0.796
−0.102
Photosynthetic assimilation (A)
Leaf color dark green
−0.357
1.431
0.615
−0.017
1.226
−0.244
Transpiration (E)
−0.381
−1.036
Leaf texture intermediate
−2.886
1.905
Water Use Efficiency (A/E)
−0.241
−0.471
Leaf texture scabrous
−2.734
2.907
Root Volume
1.117
−0.589
Foliar:Rachis biomass
0.295
−0.387
3 Rachis
color green
0.356
−0.387
3 Rachis
color yellow
−0.500
0.086
Total Biomass
−1.027
2.878
3 Rachis
color red
−0.627
0.209
Sqrt(Shoot:Root + 1)
−1.450
1.248
3 Rachis
with viscid hairs
−0.002
−0.056
0.770
0.230
3 Lenticels
striated and round
0.185
−1.519
3 Lenticels
round
0.546
−3.534
notched
1.979
1.121
0.094
0.740
0.647
0.353
3 Leafcar
3 Stem
glabrous
Proportion of trace
1 SPAD
meter reading of chlorophyll fluorescence. Initial shoot was abbreviated IS.
DISCUSSION
TABLE 4 | Proportion of J. × bixbyi Rehd. individuals assigned to the
parameter space of Juglans ailantifolia Carr. var. cordiformis, J. cinerea
L., and J. nigra L. based on the vegetative or functional adaptive trait
discriminant analyses.
Parameter space
Adaptive LDA
Vegetative LDA
0.233
0.506
Juglans cinerea L.
0.287 (0.164)
0.210
Intermediate
0.069 (0.014)
0.235
Juglans nigra L.
0.366 (0.233)
0.012
0.178
0.037
Juglans ailantifolia Carr. var. cordiformis
Novel combination
Implications of Hybrid Projections on
Hybrid Trajectory
Hybridization has been described as evolution in action, and
it can be thought of in terms of Hardy–Weinberg principles
(Anderson and Stebbins, 1954). Hybridization events introduce
variability (increase cluster volume in trait space), similar
to mutation, while selection acts on variability affecting
cluster volume and trajectory. Unequal contributions to
future generations by progenitor and hybrid populations skew
genetic contributions independent of selection, similar to
drift. Roughly equivalent contributions to future generations
can result in a highly diverse gene pool that would have
potential to evolve under selection pressure. Our example
in Juglans demonstrates how discriminant analysis can be
used to visualize the current hybrid trajectory based upon
individual hybrid responses relative to the progenitor species’
responses. Without an assessment of current demography
and selection pressures, however, it is not possible to parse
out factors associated with hybridization, notably genetic,
and demographic swamping (Todesco et al., 2016) that
determine trajectory. Independent of the underlying cause(s),
the current trajectory informs managers of the variability of
current hybrids. Use of discriminant analysis of phenotypes,
especially with non-destructive trait sampling, will allow
managers to evaluate breeding and planting efforts as indicated
Intermediate refers to parameter space between J. ailantifolia and J. cinerea
95% confidence ellipses (CEs). Novel combination refers to parameter space not
occupied by J. ailantifolia, J. cinerea, or J. nigra CEs. For the adaptive LDA,
values do not sum to one due to the overlap of J. cinerea and J. nigra CEs. The
overlap influenced J. cinerea, J. nigra, and Intermediate proportions. The value
in parenthesis represents the proportion of hybrids assigned to that parameter
space only.
two analyses, but the proportion of assignments within each
family was highly variable (Table 5). Projections of only hybrids
exhibiting initial stem color (red), the trait with the highest
loading and corresponding to J. cinerea phenotype, did not all fall
within J. cinerea parameter space in the functional adaptive trait
analysis. Of these 13 individuals four fell within J. cinerea adaptive
trait parameter space; four were outside J. ailantifolia, J. cinerea,
and J. nigra parameter space; and the remaining five within
J. nigra (2), J. ailantifolia (2), and intermediate J. ailantifolia/J.
cinerea space (1).
Frontiers in Plant Science | www.frontiersin.org
7
November 2016 | Volume 7 | Article 1741
Crystal et al.
Discriminant Analyses for Restoration
the trajectory of hybrids further. Furthermore, the functional
variability of the hybrids increases the likelihood they will be
able to colonize new areas, potentially those currently occupied
by black walnut, as indicated by hybrid occupation of the
J. nigra parameter space (Figure 4; Table 4), or become invasive
(Ellstrand and Schierenbeck, 2006).
The J. × bixbyi hybrid swarm in Figure 4 fits with current
information regarding hybrid butternut demography. Woeste
and Pijut (2009) report that F1 heterosis led to artificial selection
of hybrids by homesteaders to serve as nut trees. Increased fitness
in the F1 generation has been implicated in the establishment
of future hybrid generations (Drake, 2006), as Hoban et al.
(2009) found. Furthermore, the majority of these hybrids occur
in anthropogenically disturbed habitats (Hoban et al., 2012a),
probably due to human mediated transport of F1 individuals
(Woeste and Pijut, 2009) and the short dispersal distance
(generally < 100 m) of butternut seeds from mother trees (Hoban
et al., 2012b) in subsequent F2 and backcross generations.
Discrepancies between the two analyses in the shape of the
J. ailantifolia, J. cinerea, and J. nigra clusters are expected. The
vegetative characters were chosen specifically for their power
to distinguish the species. Adaptive traits were not chosen to
distinguish among species and there is more variation inherent
in quantitative compared to qualitative traits. This is shown by
the overlap between J. cinerea and J. nigra clusters in the adaptive
trait LDA; nevertheless, distinct clustering among species still
occurred. While cluster overlap could indicate that J. cinerea
and J. nigra are more related in terms of competition and site
preference than heartnut, all seedlings in this study were grown
under mesic conditions in which Juglans spp. typically thrive
(Goodell, 1984). These taxa would likely respond differentially
to drier or wetter conditions (Crystal and Jacobs, 2014), which
would result in greater cluster differentiation.
The hybrid swarm effect typically establishes under little or
no selection. As the majority of hybrids are found in areas
near human habitation (Hoban et al., 2012a), which are usually
mesic and favorable to most walnut species (Goodell, 1984),
fitness effects from differences in progenitor habitat tolerance
(e.g., Crystal and Jacobs, 2014) are limited. Similarly, butternut
canker disease is a weak pathogen and takes years to cause
mortality (Ostry et al., 1994). As such, increased fitness associated
with greater disease resistance (Orchard et al., 1982) is not
likely to facilitate rapid introgression of Japanese walnut alleles.
The long lifespan of forest trees reduces the probability that
small fitness contributions will spread rapidly throughout the
population (White et al., 2007). As mature butternuts continue
to succumb to age and disease, the effects of the pathogen,
which is particularly lethal to seedlings (Tisserat and Kuntz,
1982), may act to select for more disease resistant hybrids that
are also likely to contain more Japanese walnut genome. The
hybrid swarm, however, has already established, so demographic
collapse of butternut does not guarantee that hybrids will
introgress toward Japanese walnut. As Allendorf et al. (2001)
suggest, all progeny from hybrids are hybrids. Without sufficient
numbers of progenitors, hybrids will be unlikely to ever introgress
enough of the genome of butternut or Japanese walnut to be
considered equivalent to one of the progenitor species in the
TABLE 5 | Juglans × bixbyi Rehd. family breakdown of parameter space
assignments based upon the vegetative and adaptive trait discriminant
analyses.
Family1
Vegetative
Adaptive
129
Outside
2/4
J. cinerea
3/4
165
J. cinerea
2/4
J. cinerea
4/4
780
J. nigra
3/4
J. ailantifolia/Int.
2/4
781
J. ailantifolia
3/7
J. ailantifolia
5/7
782
Outside
2/32
J. cinerea/Int./J. ailantifolia
1/3
803
J. ailantifolia
2/42
J. ailantifolia
4/4
1001
J. ailantifolia/J. nigra
1/2
J. ailantifolia
2/2
1062
J. ailantifolia
2/6
J. ailantifolia/Int./J. cinerea
2/6
1093
J. ailantifolia/Int.
1/32
J. cinerea/J. ailantifolia/Outside
1/3
701
Outside
2/2
Outside/J. ailantifolia
1/2
890
J. ailantifolia/J. nigra
3/63
J. ailantifolia
4/8
1033
J. cinerea/J. nigra
2/4
J. ailantifolia
4/4
1061
J. cinerea/J. nigra
4/9
J. ailantifolia/Int.
4/9
1066
J. ailantifolia
3/6
J. ailantifolia
4/6
1064
J. cinerea/Outside
2/8
J. ailantifolia
5/8
1065
J. cinerea
4/7
Int.
4/7
1 See
3 Two
2 One
Table 2 for family provenance.
individual excluded due to missing data.
individuals excluded due to missing data.
in the Wayne and Schaffer (2016) decision tree analysis of
hybrids.
Projecting J. × bixbyi hybrids onto the parameter space
from the vegetative and functional adaptive discriminant analysis
revealed two different trajectories. The vegetative analysis
indicated that most hybrids were on Trajectory 1, introgressing
toward J. ailantifolia (Figure 3B; Table 4). As few individuals fell
onto parameter space not occupied by J. cinerea, J. ailantifolia, or
intermediate to the two, simple removal of Japanese walnut and
J. ailantifolia-like hybrids (Allendorf et al., 2001) could shift the
pool toward the then demographically more abundant butternut.
When projected on the adaptive trait discriminant analysis
parameter space, however, hybrids followed no clear pattern
(Figure 4B); they were more consistent with Trajectory 4 than
Trajectory 1. Many individuals fell outside the progenitors’ or the
intermediate parameter space, indicating that they represented
novel phenotypes (Table 4). Interestingly, the hybrid cluster
expanded more in the direction of black walnut (northeast:
Figure 4B) than it did to the southeast. This possibly reflects
better representation of hybrid phenotype combinations in this
region of the parameter space and could indicate that additional
outgroups, like Persian walnut (J. regia L.) would reveal even
greater variation in hybrids. Trajectory 1 and 4 indicate that
the use of hybrids in butternut restoration should be carefully
weighed, as the phenotypes of hybrids are widely variable and
probably unpredictable, even in uniformly mesic conditions.
Assuming that the chosen functional traits adequately describe
the autecological differences between butternut and heartnut,
the data shows that only 28.7% of hybrids might match the
physiology of J. cinerea. On the other hand, 23.3% of hybrids
would match the physiology of J. ailantifolia on those sites,
leading to survival in scenarios where butternut might not
survive, e.g., flood (Crystal and Jacobs, 2014), and altering
Frontiers in Plant Science | www.frontiersin.org
8
November 2016 | Volume 7 | Article 1741
Crystal et al.
Discriminant Analyses for Restoration
leaves (Whitham et al., 2006) can be useful for determining
the ecological equivalence of hybrids in communities. The
analysis of quantitative traits as an indication of the extent of
genetic introgression in the offspring of phenotypically distinct
progenitors is analogous to the use of neutral genetic markers,
but as Howe et al. (2003) show, populations not diverged at
neutral markers often are diverged at adaptive traits. The direct
assessment of functional traits is therefore critical to determine
ecological similarity.
The selection of traits for analysis depends on knowledge
of progenitors’ ecological function. When a particular trait is
known to be essential for functionality, multivariate techniques
are not essential for assessing hybrid ecological similarity.
This is commonly the case for natural hybrid zones, where
discrepancies in adaptive trait variation are suspected due to
existing environmental gradients (Campbell and Waser, 2001;
Kimball et al., 2008). Discriminant analysis at traits that have no
obvious adaptive importance, however, may provide additional
information on the overall variability in hybrid phenotypes
relative to progenitors. For example, Lilly et al. (2012) evaluated
loblolly (Pinus taeda L.) × shortleaf (P. echinata Mill.) F1
hybrids at several functional traits. They suggested that the
hybrid’s reduced basal crooking, a trait associated with sprouting
following fire and highly expressed in shortleaf pine, maintained
the genetic integrity of shortleaf pine under a fire regime. Now
that fire is essentially absent from the ecosystem, other traits
govern hybrid fitness relative to progenitors. Tauer et al. (2012)
report loss of genetic integrity in the demographically reduced
shortleaf pine. In this example, discriminant analysis on the
traits used in Lilly et al. (2012) could have revealed phenotypic
covariance of traits or better explained how F1 hybrids related to
progenitors.
Discriminant analysis is most useful when there is little
information on progenitor ecological function. It can
demonstrate how hybrids relate to progenitors across a
wide suite of traits and potentially, what traits distinguish the
two progenitors under a particular selection regime. This is
especially important for analysis of hybrids between native and
exotic congeners, which may have considerable overlap in their
ecological function. As demonstrated here, combinations of
many factors, not a single trait, defined differences between
butternut and Japanese walnut.
Ultimately, fitness is governed by survival and contributions
to future generations. Trees in this study were grown under low
stress conditions and only chloroplast mutants, which would
be less fit in any environment, were removed. As such, no
selection occurred, an issue with most hybrid studies (Campbell
and Waser, 2001). The growing environment, however, reflected
current hybrid conditions. Instituting an environmental selection
factor could result in greater separation of the progenitors in a
discriminant analysis because of differential response to stress. It
may also allow greater visualization of hybrid phenotypes as some
transgressive traits are only expressed under certain conditions
(Fritz et al., 2006; Ma et al., 2010). If the use of hybrids is
essential to rare species conservation (e.g., for the introduction of
resistance genes, Michler et al., 2006), identification of differential
environmental selection mechanisms is critical to removing
current selection environment (Rhymer and Simberloff, 1996;
Rieseberg and Carney, 1998).
Irrespective of trajectory (Figures 3B or 4B), the observation
that even hybrids from the same half-sibling progeny varied
widely for both trait suites (Table 5) and presented constellations
of traits unlike either progenitor indicates that general
broadcasting of hybrid trees for use in lieu of butternut should be
avoided (Michler et al., 2006). High variability among hybrids in
functional adaptive traits indicates that extreme care should be
taken in choosing individuals for a resistance breeding program.
Based upon recombination and independent assortment, certain
resistant trees could contribute high amounts of heartnut
genome. Accordingly, for so-called butternut hybrids, a white
list of seedlings screened in some fashion before outplanting, is
probably most appropriate. Discriminant analysis may prove
useful in this regard. Through assessment of functional adaptive
traits of butternut and heartnut, the traits most responsible for
differences between the taxa could be identified and utilized for
hybrid screening.
Use of Discriminant Analysis to Assess
Other Unknown Hybrid Gene Pools
As Paris and Windham (1988) reported in their morphometric
analysis of fern taxa, discriminant analysis is effective
at identifying hybrids. Their hybrids, however, were
morphologically intermediate, similar to results from F1
eucalypt hybrids (Barbour et al., 2007). As demonstrated
here, discriminant analysis can be used on known hybrids to
help evaluate the trajectory of the current hybrid population.
Introgression (Zalapa et al., 2009) and hybrid swarm effects
can be revealed with detailed genetic analysis, especially with
the advance of sequencing technology to produce the required
amount of markers (Boecklen and Howard, 1997; Elshire et al.,
2011). Yet, genetic analysis often cannot reveal phenotype, which
is ultimately what selection acts upon.
Discriminant analysis provides a relatively quick assessment
of the current hybrid trajectory and, through that, historical
selection mechanisms. If paired with detailed genetic analysis,
it may provide answers to the difficult question commonly
raised in management of hybrids: what proportion of one
progenitor’s DNA indicates that a hybrid can be considered
a member of a progenitor species (Allendorf et al., 2001)? It
may also assist in demonstrating which portions of the genome
are integral to ecological similarity, similar to studying the
genomics of adaptation. In order to provide answers to these
questions, however, assessment has to occur at an appropriate
suite of traits. The selection of traits to include in the analysis
ultimately depends on the taxa in question, desired traits, known
or hypothesized selective pressures, and the environment that
hybrids usually occupy. As such, it is not possible to provide all
of the traits necessary for a complete assessment across all taxa,
but general guidelines are presented below.
Ecological equivalence plays an important role in evaluating
hybrids for conservation, as detailed by Wayne and Schaffer
(2016). The assessment of hybrids for traits not directly associated
with fitness but related to ecosystem function, e.g., tannins in
Frontiers in Plant Science | www.frontiersin.org
9
November 2016 | Volume 7 | Article 1741
Crystal et al.
Discriminant Analyses for Restoration
hybrids that do not replicate the desired progenitors’ ecological
function. In the above example with loblolly and shortleaf pine
hybrids, discriminant analysis at the predefined traits could
be used to assess trajectory (and thus similarity) of naturally
occurring progeny under the particular selection regimes of the
habitat they are found in (i.e., a habitat typically associated with
shortleaf or loblolly pine).
This approach to trajectory assessment is most useful for
analysis of naturally occurring hybrids or later generation
artificial hybrids. It bypasses the need to separate individuals
into classes, which are highly variable (Cheng et al., 2011),
through genotyping, which is expensive and time consuming.
It can only be applied to species that are amenable to growth
in a common garden, as environmental variation will lead to
phenotypic variation. Multiple common garden tests can be used
to plot how the hybrid trajectory will change depending on the
selection environment. Gains realized from discriminant analysis
are most appreciated in long-lived species (i.e., perennial plants,
forest trees, and certain fish species) where direct assessment
of fitness through fecundity is exceptionally tedious, and
determining ecological similarity for breeding efforts extremely
useful (Woeste et al., 1998). For short-lived species (i.e., annual
plants, some insects, or other species with rapid generation
turnover) where direct assessment of fitness can be measured
through reproductive potential, discriminant analysis is not
necessary. Clearly, the applicability of this technique needs to
be assessed under multiple selection gradients and paired with
genetic analysis to determine overall hybrid function.
AUTHOR CONTRIBUTIONS
REFERENCES
Cheng, D., Vrieling, K., and Klinkhamer, P. G. L. (2011). The effect of hybridization
on secondary metabolites and herbivore resistance: implications for the
evolution of chemical diversity in plants. Phytochem. Rev. 10, 107–117. doi:
10.1007/s11101-010-9194-9
Cornelissen, J. H. C., Lavorel, S., Garnier, E., Diaz, S., Buchmann, N., Gurvich,
D. E., et al. (2003). A handbook of protocols for standardised and easy
measurement of plant functional traits worldwide. Aust. J. Bot. 51, 335–380.
doi: 10.1071/BT02124
Crystal, P. A., and Jacobs, D. F. (2014). Drought and flood stress tolerance
of butternut (Juglans cinerea) and naturally occurring hybrids: implications
for restoration. Can. J. For. Res. 44, 1206–1216. doi: 10.1139/cjfr-20140151
Diskin, M., Steiner, K. C., and Hebard, F. V. (2006). Recovery of American
Chestnut characteristics following hybridization and backcross breeding to
restore blight-ravaged Castanea dentata. Forest Ecology and Management 223,
439–447. doi: 10.1016/j.foreco.2005.12.022
Drake, J. M. (2006). Heterosis, the catapult effect and establishment success of a
colonizing bird. Biol. Lett. 2, 304–307. doi: 10.1098/rsbl.2006.0459
Ellstrand, N. C., and Schierenbeck, K. A. (2006). Hybridization as a stimulus for the
evolution of invasiveness in plants? Euphytica 148, 35–46. doi: 10.1007/s10681006-5939-3
Elshire, R. J., Glaubitz, J. C., Sun, Q., Poland, J., Kawamoto, K., Buckler, E. S., et al.
(2011). A robust, simple genotyping-by-sequencing (GBS) approach for high
diversity species. PLoS ONE 6:e19379. doi: 10.1371/journal.pone.0019379
Farlee, L., Woeste, K., Ostry, M., McKenna, J., and Weeks, S. (2010). Identification
of Butternuts and Butternut Hybrids. West Lafayette, IN: Purdue University,
1–11.
Fjellstrom, R. G., and Parfitt, D. E. (1994). Walnut (Juglans spp.) genetic diversity
determined by restriction fragment length polymorphisms. Genome 37,
690–700. doi: 10.1139/g94-097
Fox, J., and Weisberg, S. (2011). An {R} Companion to Applied Regression, 2nd Edn.
Thousand Oaks CA: Sage.
PC designed and installed the experiment, collected and analyzed
the data, and co-wrote the paper. NL contributed to data analysis
and interpretation, and co-wrote the paper. KW assisted with
data interpretation and co-wrote the paper. DJ supervised the
research, helped to design the experiment, and co-wrote the
paper.
FUNDING
Funding was provided by the Hardwood Tree Improvement and
Regeneration Center at Purdue University.
ACKNOWLEDGMENTS
We thank Justin Arsenault, Caleb Brown, James Jacobs, Matt
Kraushar, Chris Zellers, and Kate Zellers for assistance with
experimental design and measurements. James McKenna and
Brian Beheler provided seedlings and greenhouse support.
Charles Michler reviewed an earlier draft of this manuscript.
Mention of a trademark, proprietary product, or vendor does
not constitute a guarantee or warranty of the product by the
U.S. Department of Agriculture and does not imply its approval
to the exclusion of other products or vendors that also may be
suitable.
Allendorf, F. W., Leary, R. F., Spruell, P., and Wenburg, J. K. (2001). The problems
with hybrids: setting conservation guidelines. Trends Ecol. Evol. 16, 613–622.
doi: 10.1016/S0169-5347(01)02290-X
Anderson, E., and Stebbins, G. L. Jr. (1954). Hybridization as an evolutionary
stimulus. Evolution 8, 378–388. doi: 10.2307/2405784
Anderson, E., and Thompson, E. (2002). A model-based method for identifying
species hybrids using multilocus genetic data. Genetics 160, 1217–1229.
Arnold, M. L., and Martin, N. H. (2010). Hybrid fitness across time and habitats.
Trends Ecol. Evol. 25, 530–536. doi: 10.1016/j.tree.2010.06.005
Barbour, R. C., Potts, B. M., and Vaillancourt, R. E. (2007). Gene flow
between introduced and native Eucalyptus species: morphological analysis
of Tri-species and backcross hybrids involving E-nitens. Silvae Genet. 56,
127–133.
Bixby, W. G. (1919). The butternut and the Japan walnut. Am. Nut J. 10, 76–83.
Boecklen, W. J., and Howard, D. J. (1997). Genetic analysis of hybrid zones:
numbers of markers and power of resolution. Ecology 78, 2611–2616. doi:
10.1890/0012-9658(1997)078[2611:GAOHZN]2.0.CO;2
Bonner, F. T. (2008). “Juglandaceae-Walnut family,” in The Woody Plant Seed
Manual, eds F. T. Bonner and R. P. Karrfalt (Washington, DC: U.S. Department
of Agriculture, Forest Service), 601–606.
Burdett, A. N. (1979). A nondestructive method for measuring the volume of intact
plant parts. Can. J. For. Res. 9, 120–122. doi: 10.1139/x79-021
Burgess, K. S., Morgan, M., Deverno, L., and Husband, B. C. (2005). Asymmetrical
introgression between two Morus species (M. alba, M. rubra) that differ in
abundance. Mol. Ecol. 14, 3471–3483. doi: 10.1111/j.1365-294X.2005.02670.x
Campbell, D. R., and Waser, N. M. (2001). Genotype-by-environment interaction
and the fitness of plant hybrids in the wild. Evolution 55, 669–676. doi: 10.1111/
j.0014-3820.2001.tb00802.x
Chen, Z. J. (2010). Molecular mechanisms of polyploidy and hybrid vigor. Trends
Plant Sci. 15, 57–71. doi: 10.1016/j.tplants.2009.12.003
Frontiers in Plant Science | www.frontiersin.org
10
November 2016 | Volume 7 | Article 1741
Crystal et al.
Discriminant Analyses for Restoration
Fritz, R. S., Hochwender, C. G., Albrectsen, B. R., and Czesak, M. E. (2006). Fitness
and genetic architecture of parent and hybrid willows in common gardens.
Evolution 60, 1215–1227. doi: 10.1554/05-343.1
Furnier, G. R., Stolz, A. M., Mustaphi, R. M., and Ostry, M. E. (1999). Genetic
evidence that butternut canker was recently introduced into North America.
Can. J. Bot. 77, 783–785. doi: 10.1139/b99-013
Gauthier, M.-M., Zellers, K. E., Löf, M., and Jacobs, D. F. (2013). Inter- and intraspecific competitiveness of plantation-grown American chestnut (Castanea
dentata). For. Ecol. Manage. 291, 289–299.
Goodell, E. (1984). Walnuts for the Northeast. Arnoldia 44, 3–19.
Grossnickle, S. (2012). Why seedlings survive: influence of plant attributes. New
For. 43, 711–738. doi: 10.1007/s11056-012-9336-6
Hoban, S. M., McCleary, T. S., Schlarbaum, S. E., Anagnostakis, S. L., and RomeroSeverson, J. (2012a). Human-impacted landscapes facilitate hybridization
between a native and an introduced tree. Evol. Appl. 5, 720–731. doi: 10.1111/j.
1752-4571.2012.00250.x
Hoban, S. M., McCleary, T. S., Schlarbaum, S. E., and Romero-Severson, J. (2009).
Geographically extensive hybridization between the forest trees American
butternut and Japanese walnut. Biol. Lett. 5, 324–327. doi: 10.1098/rsbl.2009.
0031
Hoban, S. M., Schlarbaum, S., Brosi, S., and Romero-Severson, J. (2012b). A rare
case of natural regeneration in butternut, a threatened forest tree, is parent
and space limited. Conserv. Genet. 13, 1447–1457. doi: 10.1007/s10592-0120386-2
Howe, G. T., Aitken, S. N., Neale, D. B., Jermstad, K. D., Wheeler, N. C., and Chen,
T. H. H. (2003). From genotype to phenotype: unraveling the complexities of
cold adaptation in forest trees. Can. J. Bot. 81, 1247–1266. doi: 10.1139/b03-141
IPGRI (1994). Descriptors for Walnut: (Juglans spp.). Rome: International Plant
Genetic Resources Institute.
Kimball, S., Campbell, D. R., and Lessin, C. (2008). Differential performance of
reciprocal hybrids in multiple environments. J. Ecol. 96, 1306–1318. doi: 10.
1111/j.1365-2745.2008.01432.x
Kimura, M., Seiwa, K., Suyama, Y., and Ueno, N. (2003). Flowering system of
heterodichogamous Juglans ailanthifolia. Plant Species Biol. 18, 75–84. doi: 10.
1111/j.1442-1984.2003.00088.x
Legendre, P., and Legendre, L. (2003). Numerical Ecology: Second English Edition.
Amsterdam: Elsevier Science.
Levin, D. A., Francisco-Ortega, J., and Jansen, R. K. (1996). Hybridization and the
extinction of rare plant species. Conserv. Biol. 10, 10–16. doi: 10.1046/j.15231739.1996.10010010.x
Lilly, C. J., Will, R. E., and Tauer, C. G. (2012). Physiological and morphological
attributes of shortleaf × loblolly pine F1 hybrid seedlings: is there an
advantage to being a hybrid? Can. J. For. Res. 42, 238–246. doi: 10.1139/
x11-180
Ma, F., Zhao, C. M., Milne, R., Ji, M. F., Chen, L. T., and Liu, J. Q. (2010). Enhanced
drought-tolerance in the homoploid hybrid species Pinus densata: implication
for its habitat divergence from two progenitors. New Phytol. 185, 204–216.
doi: 10.1111/j.1469-8137.2009.03037.x
Mayer, M. S., and Mesler, M. R. (1993). Morphometric evidence of hybrid
swarms in mixed populations of Polystichum munitum and P. imbricans
(Dryopteridaceae). Syst. Bot. 18, 248–260. doi: 10.2307/2419401
Michler, C. H., Pijut, P. M., Jacobs, D. F., Meilan, R., Woeste, K. E., and Ostry, M. E.
(2006). Improving disease resistance of butternut (Juglans cinerea), a threatened
fine hardwood: a case for single-tree selection through genetic improvement
and deployment. Tree Physiol. 26, 121–128. doi: 10.1093/treephys/
26.1.121
Orchard, L. P., Kuntz, J. E., and Kessler, K. J. (1982). Reactions of Juglans species
to butternut canker and implications for disease resistance. Black Walnut for
the Future: General Technical Report NC-74. St. Paul, MN: U.S. Department
of Agriculture Forest Service. North Central Forest Experiment Station.
Ostry, M. E., Mielke, M. E., and Skilling, D. D. (1994). Butternut-Strategies
for Managing a Threatened Tree. General Technical Report NC-165. St. Paul,
MN: U.S. Department of Agriculture Forest Service. North Central Forest
Experimental Station.
Frontiers in Plant Science | www.frontiersin.org
Paris, C. A., and Windham, M. D. (1988). A biosystematic investigation of the
Adiantum pedatum complex in eastern North America. Syst. Bot. 13, 240–255.
doi: 10.3732/ajb.1100125
R Core Team (2012). R: A Language and Environment for Statistical Computing.
Vienna: R Foundation for Statistical Computing.
Rhymer, J. M., and Simberloff, D. (1996). Extinction by hybridization and
introgression. Annu. Rev. Ecol. Syst. 27, 83–109. doi: 10.1146/annurev.
ecolsys.27.1.83
Rieseberg, L. H., and Carney, S. E. (1998). Plant hybridization. New Phytol. 140,
599–624. doi: 10.1046/j.1469-8137.1998.00315.x
Seiwa, K. (2000). Effects of seed size and emergence time on tree seedling
establishment: importance of developmental constraints. Oecologia 123, 208–
215. doi: 10.1007/s004420051007
Soltis, P. S., and Soltis, D. E. (2009). The role of hybridization in plant
speciation. Annu. Rev. Plant Biol. 60, 561–588. doi: 10.1146/annurev.
arplant.043008.092039
Stout, A. B. (1928). Dichogamy in flowering plants. Bull. Torrey Bot. Club 55,
141–153. doi: 10.2307/2480605
Tauer, C. G., Stewart, J. F., Will, R. E., Lilly, C. J., Guldin, J. M., and Nelson,
C. D. (2012). Hybridization leads to loss of genetic integrity in shortleaf pine:
unexpected consequences of pine management and fire suppression. J. For. 110,
216–224. doi: 10.5849/jof.11-044
Tisserat, N. A., and Kuntz, J. E. (1982). “Epidemiology of butternut canker,” in
Proceedings of the Symposium on Black Walnut for the Future. General Technical
Report NC-74, (St. Paul MN: USDA Forest Service. North Central Experiment
Station).
Todesco, M., Pascuai, M. A., Owens, G. L., Ostevik, K. L., Moyers, B. T., Hubner, S.,
et al. (2016). Hybridization and extinction. Evol. Appl. 9, 892–908. doi: 10.1111/
eva.12367
Venables, W. N., and Ripley, B. D. (2002). Modern Applied Statistics with S, 4th
Edn. New York, NY: Springer.
Wayne, R. K., and Schaffer, H. B. (2016). Hybridization and endangered species
protection in the molecular era. Mol. Ecol. 25, 2680–2689. doi: 10.1111/mec.
13642
White, T. L., Adams, W. T., and Neale, D. B. (2007). Forest Genetics. Cambridge,
MA: CABI.
Whitham, T. G., Bailey, J. K., Schweitzer, J. A., Schuster, S. M., Banger, R. K., LeRoy,
C. J., et al. (2006). A framework for community and ecosystem genetics: from
genes to ecosystems. Nat. Rev. Genet. 7, 510–527. doi: 10.1038/nrg1877
Wilson, B. C., and Jacobs, D. F. (2006). Quality assessment of temperate zone
deciduous hardwood seedlings. New For. 31, 417–433. doi: 10.1007/s11056005-0878-8
Woeste, E., Jacobs, D. F., and McKenna, J. R. (2011). Half-sib seed source and
nursery sowing density affect black walnut (Juglans nigra) growth after 5 years.
New For. 41, 235–245. doi: 10.1007/s11056-010-9224-x
Woeste, K., McGranahan, G., and Bernatzky, R. (1998). Low correlation between
genomic and morphological introgression estimates in a walnut backcross. J.
Am. Soc. Hortic. Sci. 123, 258–263.
Woeste, K., and Pijut, P. M. (2009). The peril and potential of butternut. Arnoldia
66, 2–12.
Zalapa, J. E., Brunet, J., and Guries, R. P. (2009). Patterns of hybridization and
introgression between invasive Ulmus pumila (Ulmaceae) and native U. rubra.
Am. J. Bot. 96, 1116–1128. doi: 10.3732/ajb.0800334
Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Copyright © 2016 Crystal, Lichti, Woeste and Jacobs. This is an open-access article
distributed under the terms of the Creative Commons Attribution License (CC BY).
The use, distribution or reproduction in other forums is permitted, provided the
original author(s) or licensor are credited and that the original publication in this
journal is cited, in accordance with accepted academic practice. No use, distribution
or reproduction is permitted which does not comply with these terms.
11
November 2016 | Volume 7 | Article 1741